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import math
import os.path as osp
import pytest
import torch
from torch_geometric import EdgeIndex, Index
from torch_geometric.data import Data, RocksDatabase, SQLiteDatabase
from torch_geometric.data.database import TensorInfo
from torch_geometric.profile import benchmark
from torch_geometric.testing import has_package, withPackage
AVAILABLE_DATABASES = []
if has_package('sqlite3'):
AVAILABLE_DATABASES.append(SQLiteDatabase)
if has_package('rocksdict'):
AVAILABLE_DATABASES.append(RocksDatabase)
@pytest.mark.parametrize('Database', AVAILABLE_DATABASES)
@pytest.mark.parametrize('batch_size', [None, 1])
def test_database_single_tensor(tmp_path, Database, batch_size):
kwargs = dict(path=osp.join(tmp_path, 'storage.db'))
if Database == SQLiteDatabase:
kwargs['name'] = 'test_table'
db = Database(**kwargs)
assert db.schema == {0: object}
try:
assert len(db) == 0
assert str(db) == f'{Database.__name__}(0)'
except NotImplementedError:
assert str(db) == f'{Database.__name__}()'
data = torch.randn(5)
db.insert(0, data)
try:
assert len(db) == 1
except NotImplementedError:
pass
assert torch.equal(db.get(0), data)
indices = torch.tensor([1, 2])
data_list = torch.randn(2, 5)
db.multi_insert(indices, data_list, batch_size=batch_size)
try:
assert len(db) == 3
except NotImplementedError:
pass
out_list = db.multi_get(indices, batch_size=batch_size)
assert isinstance(out_list, list)
assert len(out_list) == 2
assert torch.equal(out_list[0], data_list[0])
assert torch.equal(out_list[1], data_list[1])
db.close()
@pytest.mark.parametrize('Database', AVAILABLE_DATABASES)
def test_database_schema(tmp_path, Database):
kwargs = dict(name='test_table') if Database == SQLiteDatabase else {}
path = osp.join(tmp_path, 'tuple_storage.db')
schema = (int, float, str, dict(dtype=torch.float, size=(2, -1)), object)
db = Database(path, schema=schema, **kwargs)
assert db.schema == {
0: int,
1: float,
2: str,
3: TensorInfo(dtype=torch.float, size=(2, -1)),
4: object,
}
data1 = (1, 0.1, 'a', torch.randn(2, 8), Data(x=torch.randn(8)))
data2 = (2, float('inf'), 'b', torch.randn(2, 16), Data(x=torch.randn(8)))
data3 = (3, float('NaN'), 'c', torch.randn(2, 32), Data(x=torch.randn(8)))
db.insert(0, data1)
db.multi_insert([1, 2], [data2, data3])
out1 = db.get(0)
out2, out3 = db.multi_get([1, 2])
for out, data in zip([out1, out2, out3], [data1, data2, data3]):
assert out[0] == data[0]
if math.isnan(data[1]):
assert math.isnan(out[1])
else:
assert out[1] == data[1]
assert out[2] == data[2]
assert torch.equal(out[3], data[3])
assert isinstance(out[4], Data) and len(out[4]) == 1
assert torch.equal(out[4].x, data[4].x)
db.close()
path = osp.join(tmp_path, 'dict_storage.db')
schema = {
'int': int,
'float': float,
'str': str,
'tensor': dict(dtype=torch.float, size=(2, -1)),
'data': object
}
db = Database(path, schema=schema, **kwargs)
assert db.schema == {
'int': int,
'float': float,
'str': str,
'tensor': TensorInfo(dtype=torch.float, size=(2, -1)),
'data': object,
}
data1 = {
'int': 1,
'float': 0.1,
'str': 'a',
'tensor': torch.randn(2, 8),
'data': Data(x=torch.randn(1, 8)),
}
data2 = {
'int': 2,
'float': 0.2,
'str': 'b',
'tensor': torch.randn(2, 16),
'data': Data(x=torch.randn(2, 8)),
}
data3 = {
'int': 3,
'float': 0.3,
'str': 'c',
'tensor': torch.randn(2, 32),
'data': Data(x=torch.randn(3, 8)),
}
db.insert(0, data1)
db.multi_insert([1, 2], [data2, data3])
out1 = db.get(0)
out2, out3 = db.multi_get([1, 2])
for out, data in zip([out1, out2, out3], [data1, data2, data3]):
assert out['int'] == data['int']
assert out['float'] == data['float']
assert out['str'] == data['str']
assert torch.equal(out['tensor'], data['tensor'])
assert isinstance(out['data'], Data) and len(out['data']) == 1
assert torch.equal(out['data'].x, data['data'].x)
db.close()
@pytest.mark.parametrize('Database', AVAILABLE_DATABASES)
def test_index(tmp_path, Database):
kwargs = dict(name='test_table') if Database == SQLiteDatabase else {}
path = osp.join(tmp_path, 'tuple_storage.db')
schema = dict(dtype=torch.long, is_index=True)
db = Database(path, schema=schema, **kwargs)
assert db.schema == {
0: TensorInfo(dtype=torch.long, is_index=True),
}
index1 = Index([0, 1, 1, 2], dim_size=3, is_sorted=True)
index2 = Index([0, 1, 1, 2, 2, 3], dim_size=None, is_sorted=True)
index3 = Index([], dtype=torch.long)
db.insert(0, index1)
db.multi_insert([1, 2], [index2, index3])
out1 = db.get(0)
out2, out3 = db.multi_get([1, 2])
for out, index in zip([out1, out2, out3], [index1, index2, index3]):
assert index.equal(out)
assert index.dtype == out.dtype
assert index.dim_size == out.dim_size
assert index.is_sorted == out.is_sorted
db.close()
@pytest.mark.parametrize('Database', AVAILABLE_DATABASES)
def test_edge_index(tmp_path, Database):
kwargs = dict(name='test_table') if Database == SQLiteDatabase else {}
path = osp.join(tmp_path, 'tuple_storage.db')
schema = dict(dtype=torch.long, is_edge_index=True)
db = Database(path, schema=schema, **kwargs)
assert db.schema == {
0: TensorInfo(dtype=torch.long, size=(2, -1), is_edge_index=True),
}
adj1 = EdgeIndex(
[[0, 1, 1, 2], [1, 0, 2, 1]],
sparse_size=(3, 3),
sort_order='row',
is_undirected=True,
)
adj2 = EdgeIndex(
[[1, 0, 2, 1, 3, 2], [0, 1, 1, 2, 2, 3]],
sparse_size=(4, 4),
sort_order='col',
)
adj3 = EdgeIndex([[], []], dtype=torch.long)
db.insert(0, adj1)
db.multi_insert([1, 2], [adj2, adj3])
out1 = db.get(0)
out2, out3 = db.multi_get([1, 2])
for out, adj in zip([out1, out2, out3], [adj1, adj2, adj3]):
assert adj.equal(out)
assert adj.dtype == out.dtype
assert adj.sparse_size() == out.sparse_size()
assert adj.sort_order == out.sort_order
assert adj.is_undirected == out.is_undirected
db.close()
@withPackage('sqlite3')
def test_database_syntactic_sugar(tmp_path):
path = osp.join(tmp_path, 'storage.db')
db = SQLiteDatabase(path, name='test_table')
data = torch.randn(5, 16)
db[0] = data[0]
db[1:3] = data[1:3]
db[torch.tensor([3, 4])] = data[torch.tensor([3, 4])]
assert len(db) == 5
assert torch.equal(db[0], data[0])
assert torch.equal(torch.stack(db[:3], dim=0), data[:3])
assert torch.equal(torch.stack(db[3:], dim=0), data[3:])
assert torch.equal(torch.stack(db[1::2], dim=0), data[1::2])
assert torch.equal(torch.stack(db[[4, 3]], dim=0), data[[4, 3]])
assert torch.equal(
torch.stack(db[torch.tensor([4, 3])], dim=0),
data[torch.tensor([4, 3])],
)
assert torch.equal(
torch.stack(db[torch.tensor([4, 4])], dim=0),
data[torch.tensor([4, 4])],
)
if __name__ == '__main__':
import argparse
import tempfile
import time
parser = argparse.ArgumentParser()
parser.add_argument('--numel', type=int, default=100_000)
parser.add_argument('--batch_size', type=int, default=256)
args = parser.parse_args()
data = torch.randn(args.numel, 128)
tmp_dir = tempfile.TemporaryDirectory()
path = osp.join(tmp_dir.name, 'sqlite.db')
sqlite_db = SQLiteDatabase(path, name='test_table')
t = time.perf_counter()
sqlite_db.multi_insert(range(args.numel), data, batch_size=100, log=True)
print(f'Initialized SQLiteDB in {time.perf_counter() - t:.2f} seconds')
path = osp.join(tmp_dir.name, 'rocks.db')
rocks_db = RocksDatabase(path)
t = time.perf_counter()
rocks_db.multi_insert(range(args.numel), data, batch_size=100, log=True)
print(f'Initialized RocksDB in {time.perf_counter() - t:.2f} seconds')
def in_memory_get(data):
index = torch.randint(0, args.numel, (args.batch_size, ))
return data[index]
def db_get(db):
index = torch.randint(0, args.numel, (args.batch_size, ))
return db[index]
benchmark(
funcs=[in_memory_get, db_get, db_get],
func_names=['In-Memory', 'SQLite', 'RocksDB'],
args=[(data, ), (sqlite_db, ), (rocks_db, )],
num_steps=50,
num_warmups=5,
)
tmp_dir.cleanup()
|
